If we want to calculate the Information Gain, the first thing we need to calculate is entropy. Entropy is calculated from a list of elements: in a text, the elements will be the characters and in an array of numeric values, the elements will be the. We can select that attribute as the Root Node. If we set b to 2, the result is expressed in bits. Once we calculate the Information Gain of every attribute, we can decide which attribute has maximum importance. Where p(i, j) represents the entries of the GLCM. Therefore ‘i’ here could be either + or (-). For simplicity’s sake let’s say we only have two classes, a positive class and a negative class. Sometimes also denoted using the letter ‘H’ Where ‘Pi’ is simply the frequentist probability of an element/class ‘i’ in our data. The temperature is a constant, and we can therefore use Equation 4.7.1 in the calculation. The Mathematical formula for Entropy is as follows - Entropy. The GLCM (corresponding to the pixel to the right) of the image above is computed as follows: glcm = np.squeeze(greycomatrix(img, distances=,Īnd finally we apply this formula to calculate the entropy: What is the entropy change of the ice Strategy Because the process is slow, we can approximate it as a reversible process. Then we read the image: img = io.imread('') Take a look at this post to learn more.Īs per your request, I'm attaching an example of how the entropy of a GLCM is computed:įirst we import the necessary modules: import numpy as np Notice that the entropy of an image is rather different from the entropy feature extracted from the GLCM (Gray-Level Co-occurrence Matrix) of an image. Where n is the number of gray levels (256 for 8-bit images), p i is the probability of a pixel having gray level i, and b is the base of the logarithm function. For example, you wouldnt calculate nutrition in the same way you. If the sample is completely homogeneous the entropy is zero and if the sample is an equally divided it has entropy of one1. Exactly how you calculate entropy is very field specific. The entropy of an image is defined as follows: As with any other state function, the change in entropy is defined as the difference between the entropies of the final and initial states: S S f S i. ID3 algorithm uses entropy to calculate the homogeneity of a sample.
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